This study reports the influence of different climatic ambient temperatures on the thermal properties of fiber-reinforced lightweight aggregate concrete (LWAC). Lightweight expanded clay aggregates (LECA) with steel (ST) and polypropylene fibers were used in the mix proportions. The steady-state thermal test was performed on concrete samples at the oven-dry state with the measurement taken at six different climatic ambient temperatures of 0 °C, 10 °C, 20 °C, 30 °C, 40 °C, and 50 °C. The results show a linear dependence of thermal conductivity, specific heat, thermal diffusivity and thermal effusivity of fiber-reinforced LWACs against the different ambient temperatures. These ambient temperature variations are discussed as a function of the thermal properties of fiber-reinforced LWAC. The thermal conductivity and thermal diffusivity decrease linearly between 0 °C and 50 °C, whilst the specific heat and thermal effusivity increase linearly between 0 °C and 50 °C. Equations with strong correlations to predict thermal properties of fiber-reinforced LWAC were proposed based on the results of this study. The significance of this research is to propose the dynamic ambient temperature-dependent thermal properties equations that can be used in the energy analysis of the buildings.

Huge amounts of fresh water are used in the concrete industry every day. The quantity and quality of water play important roles in determining the quality, strength, setting time, and durability of cement-based materials (CBMs), such as paste, mortar, and concrete. Freshwater systems are under pressure due to climate changes, industrialisation, population growth, urbanisation, and the lack of proper water resource management. The lack of potable water has resulted in the search for possible alternatives, such as seawater, treated industrial wastewater, treated sewage wastewater, carwash service station wastewater, wastewater from ready-mix concrete plants, and wastewater from the stone-cutting industry. All of these water resources can be used in concrete to achieve adequate industry standards for the physical and chemical characteristics of concrete. This study is a comprehensive review of the existing information regarding the effects of alternate water resources on the fresh, physical, strength, and durability properties of CBMs. The review shows that the research on the utilisation of wastewater in CBMs is limited. The development of different procedures and methods is urgently needed to utilise various wastewaters in concrete production. The usage of various wastewaters in concrete construction overcomes their adverse impacts on the environment and human health.

The increasing need for eco-friendly green building and creative passive design technology in response to climatic change and global warming issues will continue. However, the need to preserve and sustain the natural environment is also crucial. A building envelope plays a pivotal role in areas where the greatest heat and energy loss often occur. Investment for the passive design aspect of building envelopes is essential to address ${\mathrm{CO}}_2$ emission. This research aims to explore the suitability of using integral-monolithic structural insulation fibre-reinforced lightweight aggregate concrete (LWAC) without additional insulation as a building envelope material in a high-rise residential building in the different climatic zones of the world. Polypropylene and steel fibres in different dosages were used in a structural grade expanded clay lightweight aggregate concrete. Physical and thermal properties of fibre reinforced structural LWAC, normal weight concrete (NWC) and bricks were measured in the lab. The Autodesk@Revit-GBS simulation program was implemented to simulate the energy consumption of a 29-storey residential building with shear wall structural system using the proposed fibre-reinforced LWAC materials. Results showed that energy savings between 3.2% and 14.8% were incurred in buildings using the fibre-reinforced LWAC across various climatic regions as compared with traditional NWC and sand-cement brick and clay brick walls. In conclusion, fibre-reinforced LWAC in hot-humid tropical and temperate Mediterranean climates meet the certified Green Building Index (GBI) requirements of less than 150 kW∙h∙m⁻². However, in extreme climatic conditions of sub-arctic and hot semi-arid desert climates, a thicker wall or additional insulation is required to meet the certified green building requirements. Hence, the energy-saving measure is influenced largely by the use of fibre-reinforced LWAC as a building envelope material rather than because of building orientation.

The effects of polypropylene fibres on mortar and cement-paste cracking were investigated using different amounts of fibre fractions and eccentric rings under restrained-shrinkage conditions. Eccentric-ring tests were conducted to investigate early-age shrinkage cracks. The characteristics of restricted cementitious materials were described by evaluating the cracking time and using concrete mixtures that are less likely to crack. Different fibre-volume fractions significantly enhanced the width, area, and age of cracking. Increasing the water-cement ratio and sand percentage increased the cracking age. The eccentric-ring test showed a greater susceptibility to cracking in cement-mortar composites. The mechanical strength was assessed, and the impact of polypropylene fibres was investigated.

The integration and emerging adoption of machine learning (ML) algorithms in healthcare big data has revolutionized clinical decision-making, predictive analytics, and real-time medical diagnostics. However, the application of machine learning in healthcare big data faces computational challenges, particularly in efficiently processing and training on large-scale, high-velocity data generated by healthcare organizations worldwide. In response to these issues, this study critically reviews and examines current state-of-the-art advancements in machine learning algorithms and big data frameworks within healthcare analytics, with a particular emphasis on solutions addressing data volume and velocity. The reviewed literature is categorized into three key areas: (1) efficient techniques, arithmetic operations, and dimensionality reduction; (2) advanced and specialized processing hardware; and (3) clustering and parallel processing methods. Key research gaps and open challenges are identified based on the evaluation of the literature across these categories, and important future research directions are discussed in detail. Among the several proposed solutions are the utilization of federated learning and decentralized data processing, as well as efficient parallel processing through big data frameworks such as Apache Spark, neuromorphic computing, and multi-swarm large-scale optimization algorithms; these highlight the importance of interdisciplinary innovations in algorithm design, hardware efficiency, and distributed computing frameworks, which collectively contribute to faster, more accurate, and resource-efficient AI-driven healthcare big data analytics and applications. This research supports the UNSDG 3 (Good Health and Well-Being) and UNSDG 9 (Industry, Innovation and Infrastructure) by integration of machine learning in healthcare big data and promoting product innovation in the healthcare industry, respectively.